Hi. For my final project, I should define a Cost Function which gets 2 inputs. one of them is a 3D Tensor with size 30*7*7 and another one has size `NumberOfObjectInImage*5`

. Actually, My project is related to a detection problem. `NumberOfObjectInImage`

denotes to the number of objects which are in one image. The `5`

number denotes to the Bounding Box properties. And here is my code and I should say that I just have implemented the forward path of the cost.(It is based on Autograd Extension Tutorial):

`class MyLoss(Function): def __init__(self, S, B, l_coord, l_nobj): super(MyLoss, self).__init__() self.S = S # Number of Cell self.B = B # Number of Bouning Box self.l_coord = l_coord self.l_nobj = l_nobj def forward(self, pred_out, real_out): # pred_out: is 30*7*7 # real_out: is NumObject*5 self.save_for_backward(pred_out, real_out) po = torch.LongTensor([2]).float() sum = torch.sum pow = torch.pow sqr = torch.sqrt print(type(pred_out)) rt = real_out # Real_out pt = pred_out # Pred_out numObj = rt.size()[0] print(numObj) interval = np.linspace(0, 1, self.S + 1) cost = torch.FloatTensor([0]) for index in range(numObj): cls = rt[index,0] x = rt[index,1] y = rt[index,2] w = rt[index,3] h = rt[index,4] # Original Ground Truth box1 = (x-(w/2), y-(h/2), x+(w/2), h+(h/2)) # Select cell colS = self.indices(interval, lambda q: q > x)[0]-1 rowS = self.indices(interval, lambda q: q > y)[0]-1 # Select BBox IOU = np.ndarray(shape=(1,B)) for ind in range(B): px = pt[0, 0 + (5*ind),rowS, colS] py = pt[0, 1 + (5*ind),rowS, colS] pw = pt[0, 2 + (5*ind),rowS, colS] ph = pt[0, 3 + (5*ind),rowS, colS] box2 = (px - (pw/2), py - (ph/2), px + (pw/2), py +(ph/2))`

`IOU[0,ind] = bb_intersection_over_union(box1, box2) # Select Best BBoc sel = IOU.argmax() x_hat = pt[0, 0 + (5*sel),rowS, colS] y_hat = pt[0, 1 + (5*sel),rowS, colS] w_hat = pt[0, 2 + (5*sel),rowS, colS] h_hat = pt[0, 3 + (5*sel),rowS, colS] c_hat_obj = pt[0, 4 + (5*sel),rowS, colS] if sel == 0: c_hat_noobj = pt[0, 4 + (5),rowS, colS] else: c_hat_noobj = pt[0, 4 + (0),rowS, colS] p = torch.zeros(1,20).view(-1) p[int(cls)] = 1 p_hat = pt[0,10:,rowS, colS] cost1 = self.l_coord*(pow(x-x_hat, po)) + self.l_coord*(pow(y-y_hat, po)) print("cost1:", cost1) cost2 = pow(1-c_hat_obj,po) + self.l_nobj*pow(0-c_hat_noobj,po) print("cost2:", cost2) cost3 = self.l_coord*(pow(sqr(torch.FloatTensor([w]))-sqr(torch.FloatTensor([w_hat])),po)) + self.l_coord*(pow(sqr(torch.FloatTensor([h]))-sqr(torch.FloatTensor([h_hat])),po)) cost += (cost1 + cost2 + cost3) del cost1, cost2, cost3, p return V(cost) def backward(self, grad_cost): pred_out, real_out = self.saved_tensors grad_pred_out = grad_real_out = None return grad_pred_out, grad_real_out def indices(self, a, func): return [i for (i, val) in enumerate(a) if func(val)]`

I Would like to know this Error (`The kernel appears to have died. It will restart automatically.`

) is related to Anaconda or related to my code. Could you please help me?

Thanks